Improving resilience of sensors in planetary exploration using data-driven models

Abstract

Improving the resilience of sensor systems in space exploration is a key objective since the environmental conditions to which they are exposed are very harsh. For example, it is known that the presence of flying debris and Dust Devils on the Martian surface can partially damage sensors present in rovers/landers. The objective of this work is to show how data-driven methods can improve sensor resilience, particularly in the case of complex sensors, with multiple intermediate variables, feeding an inverse algorithm (IA) based on calibration data. [...]